Micro-genetic Algorithm Research Articles

Genetic Optimal Regression of Relevance Vector Machines for Electricity Pricing Signal Forecasting in Smart Grids

Price-directed demand in smart grids operating within deregulated electricity markets calls for real-time forecasting of the price of electricity for the purpose of scheduling demand at the nodal level (e.g., appliances, machines, and devices) in a way that minimizes energy cost to the consumer. In this paper, a novel hybrid methodology for electricity price forecasting is introduced and applied on a set of real-world historical data taken from the New England area. The proposed approach is implemented in two steps. In the first step, a set of relevance vector machines (RVMs) is adopted, where each RVM is used for individual ahead-of-time price prediction. In the second step, individual predictions are aggregated to formulate a linear regression ensemble, whose coefficients are obtained as the solution of a single objective optimization problem. Thus, an optimal solution to the problem is found by employing the micro-genetic algorithm and the optimized ensemble is employed for computing the final price forecast. The performance of the proposed methodology is compared with performance of autoregressive-moving-average and naive forecasting methods, as well as to that taken from each individual RVM. Results clearly demonstrate the superiority of the hybrid methodology over the other tested methods with regard to mean absolute error for electricity signal pricing forecasting.

Detection of masses in mammograms with adaption to breast density using genetic algorithm, phylogenetic trees, LBP and SVM

Breast cancer is the second commonest type of cancer in the world, and the commonest among women, corresponding to 22% of the new cases every year. This work presents a new computational methodology, which helps the specialists in the detection of breast masses based on the breast density. The proposed methodology is divided into stages with the objective of overcoming several difficulties associated with the detection of masses. In many of these stages, we brought contributions to the areas. The first stage is intended to detect the type of density of the breast, which can be either dense or non-dense. We proposed an adaptive algorithm capable of analyzing and image and telling if it is dense or non-dense. The first stage consists in the segmentation of the regions that look like masses. We propose a novel use of the micro-genetic algorithm to create a texture proximity mask and select the regions suspect of containing lesions. The next stage is the reduction of false positives, which were generated in the previous stage. To this end, we proposed two new approaches. The first reduction of false positives used DBSCAN and a proximity ranking of the textures extracted from the ROIs. In the second reduction of false positives, the resulting regions have their textures analyzed by the combination of Phylogenetic Trees, Local Binary Patterns and Support Vector Machines (SVM). A micro-genetic algorithm was used to choose the suspect regions that generate the best training models and maximize the classification of masses and non-masses used in the SVM. The best result produced a sensitivity of 92.99%, a rate of 0.15 false positives per image and an area under the FROC curve of 0.96 in the analysis of the non-dense breasts; and a sensitivity of 83.70%, a rate of 0.19 false positives per image and an area under the FROC curve of 0.85, in the analysis of the dense breasts.

Kursawe and ZDT functions optimization using hybrid micro genetic algorithm (HMGA)

A hybrid micro genetic algorithm (HMGA) is proposed for Pareto optimum search focusing on the Kursawe and ZDT test functions. HMGA is a fusion of the micro genetic algorithm (MGA) and the elitism concept of fast Pareto genetic algorithm. The effectiveness of HMGA in Pareto optimal convergence was investigated with two performance indicators (i.e. generational distance and spacing). To measure HMGA's performance, a comparison study was conducted between HMGA and MGA. In this work, HMGA is outperformed MGA in the search for Pareto optimal front and capable of solving different difficulty of MOPs.

Friction and Colburn factor correlations and shape optimization of chevron-type plate heat exchangers

The unsteady numerical analysis with a large-eddy simulation (LES) has been described to examine the characteristics of pressure drop and heat transfer for chevron-type plate heat exchangers. For validation of the numerical results, experiments were carried out; good agreement was found, with a maximum error of 10%. Correlations of friction factor and Colburn factor were determined by taking various geometric parameters (chevron angle β, pitch p, and height h) and working fluids into account. Design optimization was also conducted by using the micro-genetic algorithm and considering the JF factor as an objective function. The optimal points (β = 66.5° and p/h = 2.73) were almost constant, regardless of the Reynolds number.

Damage Identification of Truss Structures Based on Force Method

AbstractAn computationally efficient damage identification technique for the planar and space truss structures is presented based on the force method and the micro genetic algorithm. For this purpose, the general equilibrium equations and the kinematic relations in which the reaction forces and the displacements at nodes are take into account, respectively, are formulated. The compatibility equations in terms of forces are explicitly presented using the singular value decomposition (SVD) technique. Then governing equations with unknown reaction forces and initial elongations are derived. Next, the micro genetic algorithm (MGA) is used to properly identify the site and extent of multiple damage cases in truss structures. In order to verify the accuracy and the superiority of the proposed damage detection technique, the numerical solutions are presented for the planar and space truss models. The numerical results indicate that the combination of the force method and the MGA can provide a reliable tool to accurately and efficiently identify the multiple damages of the truss structures.

Shape Optimization of Wideband Antennas for Microwave Energy Harvesters Using FDTD

This paper presents parameter and topology optimizations of wideband antennas for microwave energy harvesters based on finite difference time-domain computations. The antenna shapes are optimized to reduce return losses in a specific frequency band using micro-genetic algorithm. The shape parameters of a two-arm planar spiral antenna are optimized. In addition, the topology optimization is performed using normalized Gaussian network, where spatial symmetries are assumed. It is shown that the return losses of the optimized antennas are <−10 dB from 1 to 2 GHz in which they have isotropic directivity.

Microgenetic optimization algorithm for optimal wavefront shaping.

One of the main limitations of utilizing optimal wavefront shaping in imaging and authentication applications is the slow speed of the optimization algorithms currently being used. To address this problem we develop a microgenetic optimization algorithm (μGA) for optimal wavefront shaping. We test the abilities of the μGA and make comparisons to previous algorithms (iterative and simple-genetic) by using each algorithm to optimize transmission through an opaque medium. From our experiments we find that the μGA is faster than both the iterative and simple-genetic algorithms and that both genetic algorithms are more resistant to noise and sample decoherence than the iterative algorithm.

Position and Thickness Optimization of Ribs for Ventilation Covering Using the Micro Genetic Algorithm with an Interpolated Smooth Objective Function

Structures with supporting ribs are adopted in many fields of engineering. These ribs are attached to the main plate or shell to increase stiffness and reduce the stresses of a structure. Currently, much research in structural optimization has been devoted to size or thickness optimizations. In this study, the discrete positions of the ribs of a structure are optimized in addition to their thicknesses. The objective function, which is the total weight of a structure, is a continuous function with respect to the thickness of the ribs. However, it is a stepwise function of a dimensionless variable, which represents the set of positions of the ribs. Because of this stepwise objective function, the gradient method of optimization is not applicable. Therefore, we applied the micro genetic algorithm (MGA), which does not need derivatives of the objective function. To accelerate the rate of convergence, the stepwise objective function is interpolated to a smooth artificial objective function that does not alter the optimal solution.

Scheme-Based Optimization of Land Surface Model Using a Micro-Genetic Algorithm: Assessment of Its Performance and Usability for Regional Applications

Scheme-Based Optimization of Land Surface Model Using a Micro-Genetic Algorithm: Assessment of Its Performance and Usability for Regional Applications

Field Application of Cable Tension Estimation Technique Using the h-SI Method

This paper investigates field applicability of a new system identification technique of estimating tensile force for a cable of long span bridges. The newly proposed h-SI method using the combination of the sensitivity updating algorithm and the advanced hybrid microgenetic algorithm can allow not only avoiding the trap of local minimum at initial searching stage but also finding the optimal solution in terms of better numerical efficiency than existing methods. First, this paper overviews the procedure of tension estimation through a theoretical formulation. Secondly, the validity of the proposed technique is numerically examined using a set of dynamic data obtained from benchmark numerical samples considering the effect of sag extensibility and bending stiffness of a sag-cable system. Finally, the feasibility of the proposed method is investigated through actual field data extracted from a cable-stayed Seohae Bridge. The test results show that the existing methods require precise initial data in advance but the proposed method is not affected by such initial information. In particular, the proposed method can improve accuracy and convergence rate toward final values. Consequently, the proposed method can be more effective than existing methods in terms of characterizing the tensile force variation for cable structures.

FPGA-Based Embedded System Architecture for Micro-Genetic Algorithms Applied to Parameters Optimization in Motion Control

Meta-heuristic techniques are powerful tools used to find an optimal solution for complex problems to which classical techniques find difficult to solve. The features among all the met ...

Benthic biomass size spectra in shelf and deep-sea sediments

Abstract. The biomass distributions of marine benthic metazoans (meio- to macro-fauna, 1 μg–32 mg wet weight) across three contrasting sites were investigated to test the hypothesis that allometry can consistently explain observed trends in biomass spectra. Biomass (and abundance) size spectra were determined from observations made at the Faroe–Shetland Channel (FSC) in the Northeast Atlantic (water depth 1600 m), the Fladen Ground (FG) in the North Sea (150 m), and the hypoxic Oman Margin (OM) in the Arabian Sea (500 m). Observed biomass increased with body size as a power law at FG (scaling exponent, b = 0.16) and FSC (b = 0.32), but less convincingly at OM (b = 0.12 but not significantly different from 0). A simple model was constructed to represent the same 16 metazoan size classes used for the observed spectra, all reliant on a common detrital food pool, and allowing the three key processes of ingestion, respiration and mortality to scale with body size. A micro-genetic algorithm was used to fit the model to observations at the sites. The model accurately reproduces the observed scaling without needing to include the effects of local influences such as hypoxia. Our results suggest that the size-scaling of mortality and ingestion are dominant factors determining the distribution of biomass across the meio- to macrofaunal size range in contrasting marine sediment communities. Both the observations and the model results are broadly in agreement with the "metabolic theory of ecology" in predicting a quarter power scaling of biomass across geometric body size classes.

Tensile force identification in cable-stayed structures: Hybrid system identification algorithm and experimental verification

In this study, we investigate a method to detect tensile forces in cable-stayed structures using the combined sensitivity updating method and the advanced hybrid microgenetic algorithm. The proposed method allows us not only to avoid the trap of minimum at initial searching stage but also to find their final solutions in better numerical efficiency. The validity of the technique is numerically verified using a set of dynamic data obtained from a simulation of the cable model modeled using the finite element method. Then, the hybrid algorithm is applied to vibrating sagged cables in the laboratory scale test. The results obtained are in good agreement with the semi-analytical solutions and experimental results reported by other investigators. The results indicate that the new method is computationally efficient in characterizing the tensile force variation for cable-stayed structures.

Assessing optimal set of implemented physical parameterization schemes in a multi-physics land surface model using genetic algorithm

Abstract. Optimization of land surface models has been challenging due to the model complexity and uncertainty. In this study, we performed scheme-based model optimizations by designing a framework for coupling "the micro-genetic algorithm" (micro-GA) and "the Noah land surface model with multiple physics options" (Noah-MP). Micro-GA controls the scheme selections among eight different land surface parameterization categories, each containing 2–4 schemes, in Noah-MP in order to extract the optimal scheme combination that achieves the best skill score. This coupling framework was successfully applied to the optimizations of evapotranspiration and runoff simulations in terms of surface water balance over the Han River basin in Korea, showing outstanding speeds in searching for the optimal scheme combination. Taking advantage of the natural selection mechanism in micro-GA, we explored the model sensitivity to scheme selections and the scheme interrelationship during the micro-GA evolution process. This information is helpful for better understanding physical parameterizations and hence it is expected to be effectively used for further optimizations with uncertain parameters in a specific set of schemes.

Neuro-genetic optimization of laminar slot jets impinging on a moving surface

In this work, heat transfer from a moving surface due to series of impinging slot jets under laminar conditions has been optimized. For this study numerical investigations were carried out initially using Ansys Fluent 14 and these results were used to train an artificial neural network (ANN). This trained network was integrated into Micro-Genetic Algorithm to get the optimum parameters for better heat transfer from the surface, an optimization procedure proposed by Madadi and Balaji. Pitch of the jets (P), height of the jets (H) and the non-dimensional surface velocity (Vs) were chosen as dependent variables for optimum heat transfer. 99 simulations were performed by changing above parameters for each Reynolds number, Re of 100 and 200 were used for case study. Imposition of surface velocity strongly affects the heat transfer magnitude and distribution following a change in flow structure. The performance of Micro-Genetic Algorithm (μGA) was also compared with standard Genetic Algorithm (GA); it shows that μGA reaches optimum in less than half the time of standard GA. The optimum results show that the pitch of the jets, height of the jets and surface velocity should be as low as possible.

Damage detection of truss structures using two-stage optimization based on micro genetic algorithm

A simple and efficient two-stage optimization procedure is proposed to properly identify the sites and the extent of multiple damages in truss structures. In the first stage, the most potentially damaged elements are identified using an anti-optimization (AO) technique. In the second stage, a micro genetic algorithm (MGA) is performed to accurately determine the actual damage extents based on a priori knowledge from the first stage. The correctness and effectiveness of the proposed algorithm are proved by two illustrated test examples: the planar and space truss models with the numerically simulated data. The numerical results show the computational advantages of the proposed method to precisely determine the sites and the extent of multiple damages of truss structures.

Decentralized diagnostics based on a distributed micro-genetic algorithm for transducer networks monitoring large experimental systems.

Evolutionary approach to centralized multiple-faults diagnostics is extended to distributed transducer networks monitoring large experimental systems. Given a set of anomalies detected by the transducers, each instance of the multiple-fault problem is formulated as several parallel communicating sub-tasks running on different transducers, and thus solved one-by-one on spatially separated parallel processes. A micro-genetic algorithm merges evaluation time efficiency, arising from a small-size population distributed on parallel-synchronized processors, with the effectiveness of centralized evolutionary techniques due to optimal mix of exploitation and exploration. In this way, holistic view and effectiveness advantages of evolutionary global diagnostics are combined with reliability and efficiency benefits of distributed parallel architectures. The proposed approach was validated both (i) by simulation at CERN, on a case study of a cold box for enhancing the cryogeny diagnostics of the Large Hadron Collider, and (ii) by experiments, under the framework of the industrial research project MONDIEVOB (Building Remote Monitoring and Evolutionary Diagnostics), co-funded by EU and the company Del Bo srl, Napoli, Italy.

Evaluation of Time Evolution of Mechanical Parameters of Polymeric Pipes by Unsteady Flow Runs

The paper presents the results of the calibration, by means of a microgenetic algorithm, of the mechanical parameters of high-density polyethylene pipes, to reproduce experimental unsteady flow runs in an installation consisting primarily of a steel pipe and of an additional polyethylene pipe. The mechanical parameters of viscoelastic models are estimated using both one-dimensional and quasi-two-dimensional models. Two sets of runs carried out at a distance of about 12 years are considered. The calibration of a three-parameter Kelvin-Voigt model provides evidence of the impossibility of obtaining general results. The use of a model with a single Kelvin-Voigt element (two-parameter model) gives good results in terms of comparison between numerical and experimental results for both pressure head and velocity. The calibration for the second series of tests shows increased deformability of the material (the reduction of the elastic modulus is more than 30%). The calibrated values of the retardation time result linearly correlated to the oscillations period, increasing with time due to the reduction of the elastic modulus. The obtained results allow evaluation of the viscoelastic parameters starting only with the knowledge of the equivalent elastic modulus.

An indirect approach for discharge estimation: A combination among micro-genetic algorithm, hydraulic model, and in situ measurement

To develop a flood forecasting system, estimating the discharge hydrograph is essential. In general, discharges at gauged river sites are calculated by applying simple methods such using the relationship of measured stages to discharges, namely rating curves, or multiplying mean velocity with flow cross-sectional area. The flow cross-sectional area can be determined using measured stages from river geometry surveys. The mean velocity is considered to be the measured surface velocity multiplied by a conversion factor. The conversion factor can be estimated by using the regression approach given a known discharge. However, to obtain discharge for extreme events is difficult. Extrapolation was necessarily made among known discharges to “guess” the discharge hydrograph during floods. Therefore, a novel approach which combines micro-genetic algorithm (µGA), a one-dimensional (1-D) flood routing model, and onsite instrumentation is being proposed to obtain the optimal conversion factor, and therefore the discharge hydrograph. This approach was validated using two events: one synthetic test and one recorded event at Yilan River. The results showed that µGA efficiently converged to an optimal conversion factor which showed a less than five percent difference when comparing with synthetic versus observed values. A sensitivity analysis was also conducted to assess the impact of the quantity of selected gauged stations on the value of optimal factor in the optimization process.

Detection ofTensile Forces in Cable-Stayed Structures Using the Advanced Hybrid Micro-Genetic Algorithm

Detection ofTensile Forces in Cable-Stayed Structures Using the Advanced Hybrid Micro-Genetic Algorithm